Temporal convolutional networks predict dynamic oxygen uptake response from wearable sensors across exercise intensities
Abstract Oxygen consumption ( $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 ) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, $$\dot{\,{{\mbox{V}}}}{{{\mbox{O}}}}_{2}$$ V ̇ O 2 monitoring is largely limited to specialized...
Enregistré dans:
Auteurs principaux: | Robert Amelard, Eric T. Hedge, Richard L. Hughson |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/5b7b3752512d45698685dc8c613a2949 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Out-of-clinic measurement of sweat chloride using a wearable sensor during low-intensity exercise
par: Dong-Hoon Choi, et autres
Publié: (2020) -
Investigating sources of inaccuracy in wearable optical heart rate sensors
par: Brinnae Bent, et autres
Publié: (2020) -
Harnessing consumer smartphone and wearable sensors for clinical cancer research
par: Carissa A. Low
Publié: (2020) -
Response To: Investigating sources of inaccuracy in wearable optical heart rate sensors
par: Peter J. Colvonen
Publié: (2021) -
Reply: Matters Arising ‘Investigating sources of inaccuracy in wearable optical heart rate sensors’
par: Brinnae Bent, et autres
Publié: (2021)